cs.CYFeb 22, 2026
SaveDefining AI Models and AI Systems: A Framework to Resolve the Boundary Problem
Organizations: AI Governance Exchange · Independent · University of British Columbia · Oxford Internet Institute, University of Oxford ·
Abstract
Emerging AI regulations assign distinct obligations to different actors along the AI value chain (e.g., the EU AI Act distinguishes providers and deployers for both AI models and AI systems), yet the foundational terms "AI model" and "AI system" lack clear, consistent definitions. Through a systematic review of 896 academic papers and a manual review of over 80 regulatory, standards, and technical or policy documents, we analyze existing definitions from multiple conceptual perspectives. We then trace definitional lineages and paradigm shifts over time, finding that most standards and regulatory definitions derive from the OECD's frameworks, which evolved in ways that compounded rather than resolved conceptual ambiguities. The ambiguity of the boundary between an AI model and an AI system creates practical difficulties in determining obligations for different actors, and raises questions on whether certain modifications performed are specific to the model as opposed to the non-model system components. We propose conceptual definitions grounded in the nature of models and systems and the relationship between them, then develop operational definitions for contemporary neural network-based machine-learning AI: models consist of trained parameters and architecture, while systems consist of the model plus additional components including an interface for processing inputs and outputs. Finally, we discuss implications for regulatory implementation and examine how our definitions contribute to resolving ambiguities in allocating responsibilities across the AI value chain, in both theoretical scenarios and case studies involving real-world incidents.
Figures & tables
Figure 1: Overview of conceptual perspectives for definitions of AI model and AI system .
Figure 2: Definitional lineage of terms related to AI models and AI systems
Figure 3: OECD (2019b) (top) versus Russell and Norvig (2009) (bottom)
| AI system / Intelligent Agent | “An AI system is a machine-based system that is capable of influencing the Environment by making recommendations, predictions or decisions for a given set of Objectives. It does so by utilising machine and/or human-based inputs/data to: i) perceive real and/or virtual environments; ii) abstract such perceptions into models manually or automatically; and iii) use Model Interpretations to formulate options for outcomes.” ( OECD, 2019b , p. 7) |
| AI Operational Logic (p. 6) | “The key power of an AI system resides in its Operational Logic, which, for a given set of objectives and based on input data from Sensors, provides output for the Actuators — as recommendations, predictions or decisions — that are capable of influencing the state of the Environment.” ( OECD, 2019b , p. 6) |
| Model (p. 6-7) | “A Model is an actionable representation of all or part of the external environment of an AI system that describes the environment’s structure and/or dynamics. The model represents the core of an AI system. A model can be based on data and/or expert knowledge, by humans and/or by automated tools like machine learning algorithms. Model Interpretation is the process of deriving an outcome from a model.” ( OECD, 2019b , p. 7) “Model (a data object constructed by the Model Building process)” ( OECD, 2019b , p. 6) |
Table 1: Key definitions related to AI models and AI systems from OECD (2019b)
| Russell & Norvig’s Term | OECD (2019b) Equivalent | Key Difference in Taxonomy | Primary Object Type |
| Agent / Rational Agent / Intelligent Agent | AI System / Intelligent Agent | The definitions are almost identical. | OECD: A mixture of real, digital, and abstract objects. |
| Agent Function | AI Operational Logic | Near-identical definitions (mapping inputs to outputs) | Abstract |
| Agent Program | (Most analogous to) Model | Agent program is concrete software; OECD model is “data object” | Digital |
| World Model (of a model-based agent) | A specific type of Model | Russell and Norvig (2009) : specific to subset of agents (model-based); OECD: implied for all systems | Digital or Abstract |
Table 2: Relationship and differences between Russell and Norvig (2009) term and OECD (2019b)
Figure 4: OECD (2022) ’s conceptualisation of the term AI model
| Aspect | OECD (2019b) | OECD (2022) | Consequences |
| Core Definition | An actionable representation of all or part of the external environment of an AI system. | An actionable representation of all or part of the external context or environment of an AI system. | Slight expansion from “environment” to “context.” |
| Relationship with Environment | The model […] describes the environment’s structure and/or dynamics. | The model represents, describes, and interacts with real or virtual environments. | Introduces the incorrect idea that a model can “interact.” |
| Relation to System | Clearly a component within the system, representing its environment. | Blurred; diagrammatically equated with the system’s core logic, definitionally remains a representation. | Fundamental tension between the visual framework and its textual definitions. |
Table 3: Differences and similarities between terminologies in OECD (2019b) and OECD (2022)
Figure 5: OECD (2024) ’s conceptual diagram
| Concept | Russell and Norvig (2009) | OECD (2019b) | OECD (2022) (Text) | OECD (2022) (Image) | OECD (2024) |
| World model of a model-based agent | Yes | Yes | Yes | Sometimes | Sometimes |
| Representation of (part of) the external environment | Yes | Yes | Yes | Sometimes | Sometimes |
| Core component of an AI system | Sometimes (model-based only) | Yes | Yes | Yes | Yes |
| Abstract mapping between system input and output | No (Agent Function) | No (AI Operational Logic) | Sometimes | Yes | Yes |
| Policy/Q-function of a model-free agent | No | No | No | Yes | Yes |
Table 4: Conceptions of AI model across different frameworks
Figure 6: AI system containing an AI model
Figure 7: AI system containing an AI model with other non-model components
| Core component of AI system to make inferences | Parameters change during the build phase | Usually remain fixed after deployment | Part of AI model ? | |
| Model weights | Yes | Yes | Yes | Yes |
| Input prompt | Yes | Not parameters | Not parameters | No |
| Activations | Not a component - they are dynamic intermediate computation results | Not parameters | Not parameters | No |
| System prompt | Not a component - it’s an input of the model | Not parameters | Not parameters | No |
| RAG | Not a component - it’s a technique; but the associated knowledge base used by RAG can be a core component of the system | Not parameters | Not parameters | No |
| Temperature setting | Not a component - it’s a setting | Not parameters | Not parameters | No |
Table 5: Assessment of whether an element is part of an AI model according to the three candidate criteria from OECD (2024)
| Component of AI system | Trained parameters | Part of AI model ? | |
| Model weights | Yes | Yes | Yes |
| Input prompt | No | No | No |
| Activations | Not really | No | No |
| System prompt | No | No | No |
| RAG | No for the technique itself; yes for the associated knowledge base, if implemented as part of the system | No | No |
| Temperature setting | No | No | No |
Table 6: Assessment of whether an element is part of an AI model according to our candidate criteria
| Conceptual definition | Summarised operational definition for contemporary AI | |
| AI model | A computational construct that makes inferences from inputs to produce outputs and forms (or is intended to form) the reasoning or knowledge core of an AI system. | Trained parameters and their architecture, understood as the computational specification necessary to use them for inference. |
| AI system | A machine-based system that makes inferences from inputs from physical or virtual environments to produce outputs that influence physical or virtual environments. | One or more AI models, an interface for receiving inputs from and delivering outputs to an environment, and the configuration connecting these and any additional components. |
Table 7: Our proposed conceptual and summarised operational definitions of AI model and AI system
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
| Criterion | Keywords |
| Population | AI model and AI system Synonyms: Machine learning system, autonomous system, automated system, algorithmic system Machine learning model, foundation model, algorithmic model, predictive model AI application, machine learning application, AI solution, machine learning solution, AI services |
| Intervention | Definition Synonyms: Ontology, conceptual framework, taxonomy, terminology, scope |
| Comparison | Not applicable |
| Outcome | Find different definitions, clarity on differences |
| Context | Computer science, engineering, social sciences, decision sciences, business, management and accounting, arts and humanities, multidisciplinary, psychology |
Table 8: SLR Keywords from PICOC Criteria
| Category | Selection Criteria |
| Period | From 2012 to present |
| Language | English |
| Relevance to research question | The article provides a definition of AI model , AI system , or a concept synonymous with these terms |
| Type of source | Articles, reviews and conference papers |
Table 9: SLR Inclusion and Exclusion Criteria
Figure 8: SLR Search String
Figure 9: PRISMA Flow Diagram of the SLR
| Authors | Term defined | Source of definition |
| Mühlhoff and Ruschemeier [2025] | Machine learning model Machine learning model | Authors themselves Mühlhoff and Ruschemeier [2024] |
| Kallab et al. [2024] | Machine learning model Machine learning model | Authors themselves Janiesch et al. [2021] |
| Fernández-Llorca et al. [2025] | AI model Machine learning model | ISO/IEC [2022e] ISO/IEC [2022e] |
| Lu et al. [2024] | AI model | Authors themselves |
| Hupont et al. [2024] | AI model AI model | ISO/IEC [2022e] OECD [2024] |
| Park et al. [2024] | AI model | Authors themselves |
Table 10: SLR Findings for AI model and machine learning model
| Authors | Term defined | Source of definition |
| Ojanen [2025] | AI system | European Parliament and Council of the European Union [2024] |
| Müller et al. [2025] | AI system | Authors themselves |
| Fernández-Llorca et al. [2025] | AI system AI system AI system AI system | OECD [2019a] European Commission [2021] European Parliament [2023] European Parliament and Council of the European Union [2024, Article 3(1)] |
| Lu et al. [2024] | AI model | Authors themselves |
| Hupont et al. [2024] | AI system AI system | Authors themselves OECD [2019a] |
| Buiten [2024] | AI system AI system AI system | Authors themselves Buiten et al. [2023] European Parliament and Council of the European Union [2024] |
Table 11: SLR Findings for AI system and “machine learning system”
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